Energy storage power station integrated type selection, site selection and capacity determination method

Abstract

The application relates to an energy storage power station integrated type selection, site selection and capacity determination method, which comprises the following steps: writing a node configuration scheme index matrix to represent the index benefits of different types of batteries in various node configurations in a network; for the obtained node configuration scheme index matrix corresponding to different types of energy storage batteries, comprehensive evaluation is conducted on the matrix respectively to form an energy storage battery score matrix; all battery type score matrices are obtained to form an optimal scheme index matrix of all battery types, and optimal site selection and capacity determination schemes for the same type of energy storage battery are realized; weights of power grid benefit indexes after configuration of each energy storage power station are determined according to requirements of investors, and the optimal configuration index set of different energy storage batteries is scored and selected according to the weights, so that the integrated selection of optimal configuration schemes among different energy storage batteries is realized.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of new energy, and particularly relates to an integrated type selection, site selection and capacity determination method for an energy storage power station. BACKGROUND

[0002] The proportion of photovoltaic power generation connected to the current power grid is continuously increasing. Photovoltaic power generation is widely used in power generation and frequency modulation due to its high power generation efficiency, environmental friendliness and low cost. However, due to the randomness and volatility of photovoltaic power generation, sudden large amounts of photovoltaic power generation can cause the power grid to be unable to absorb this part of power. Therefore, energy storage devices are often installed in photovoltaic power stations. When the photovoltaic power generation is large, the excess power is used to charge the energy storage, and when the photovoltaic power generation is less than the demand, the energy storage is discharged. The use of energy storage devices can reduce the impact of photovoltaic power generation on power station operation. The installation of energy storage is the most effective and most widely used method to cope with the randomness and volatility of new energy power generation. However, with the popularization of energy storage devices, some problems have arisen.

[0003] In the early planning of an energy storage power station, the selection of energy storage batteries has a long-term impact on the operation efficiency of the energy storage power station. Energy storage batteries are divided into power-type energy storage batteries and capacity-type energy storage batteries. The power-type energy storage batteries can provide large power support in a short time, but the energy storage capacity and service life are often limited. The capacity-type energy storage batteries are suitable for long-term power grid support and can provide large total capacity support for a period of time, but their power is smaller than that of the power-type energy storage batteries. On the other hand, the site selection and capacity configuration of the energy storage power station also have a long-term impact on the operation support capacity of the energy storage power station in a region, which should be considered together in the planning stage.

[0004] Existing energy storage power station planning and configuration methods mostly only consider site selection and capacity determination or battery selection, without considering the integration of battery type and site selection and capacity determination of the energy storage power station. Such planning technology has problems in the long term. Power-type or capacity-type energy storage batteries will affect the main power consumers and customers of the energy storage power station, and even different performances exist between power-type and capacity-type energy storage batteries. There is an urgent need for an integrated type selection, site selection and capacity determination method for an energy storage power station. SUMMARY

[0005] The present application uses an integrated type selection, site selection and capacity determination method for an energy storage power station to optimize the type selection, site selection and capacity determination method for the planning and configuration of an energy storage power station. The specific technical solutions are as follows:

[0006] An integrated type selection, site selection and capacity determination method for an energy storage power station includes the following processes:

[0007] Step 1: In the planning process of the target power grid energy storage power station, for the selectable energy storage battery models, write down their node configuration scheme index matrix to represent the index benefits of different types of batteries when configured at each node in the network; Specifically, the following steps are included:

[0008] Step 1.1: For m types of energy storage batteries as selectable energy storage power station battery types, use M = {M1, M2, …, Mm} to represent the battery type set; m

[0009] Step 1.2: For the first type of battery M1 in M, establish the node configuration scheme index matrix for this type of battery; The matrix establishment steps are as follows:

[0010] Step 1.2.1: Obtain the node information of the target power grid with a total of b power grid nodes, the power grid network structure information of a total of N power grid lines, obtain the target power grid network structure data, power grid line parameters, and target power grid power generation and load power consumption of a total of L loads;

[0011] Step 1.2.2: Establish an energy storage power station at the i-th power grid node of the target power grid, input the node information of the target power grid with a total of b power grid nodes, the power grid network structure information, obtain the target power grid network structure data, power grid line parameters, and target power grid power generation and load conditions as input data into the optimal power flow analysis method for calculation, and the objective function is set to minimize the power grid voltage fluctuation, network loss, and power grid load shedding; The formula is as follows:

[0012]

[0013] In the formula, F = min (k) represents the minimized objective function composed of power grid voltage fluctuation, network loss, and power grid load shedding; k represents a temporary variable in the summation function; b represents the number of target power grid nodes; U min represents the node voltage at the k-th node, U k represents the rated node voltage at the k-th node; N represents the total number of target power grid lines; P k，N represents the line loss on the k-th line; L represents the total number of loads in the target power grid; P k represents the actual power consumption of the k-th load; P l，k represents the ideal power consumption of the k-th load; l，k，N

[0014] Through the optimal power flow analysis method, the capacity of the energy storage power station that needs to be configured at the i-th node to ensure that the power grid operates in the most optimal benefit condition can be obtained;

[0015] ​​Step 1.2.3: After the optimal power flow analysis method is run, n different operation benefit indicators of the power grid can be calculated to form the energy storage configuration scheme evaluation index set λ of the i-th node when the energy storage power station is configured i，M1 1，i，M1 2，i，M1 n，i，M1 , where each λ represents a certain indicator, and these indicators together form the energy storage configuration scheme evaluation index set of the i-th node when the energy storage power station is configured;

[0016] Step 1.2.4: For all b power grid nodes in the target power grid, repeat steps 1.2.2 and 1.2.3, so that all b power grid nodes in the power grid generate a corresponding energy storage configuration scheme evaluation index set, and these b energy storage configuration scheme evaluation index sets form the node configuration scheme index matrix D of the first type of battery M1 when the energy storage power station is configured M1 ; the node configuration scheme index matrix The formula is as follows:

[0017]

[0018] In the formula, , which represents the node configuration scheme index matrix of the first type of battery M1 when the energy storage power station is configured , which represents the energy storage configuration scheme evaluation index set of the i-th node when the energy storage power station is configured using the first type of battery M1 in M; , which represents the value of the n-th type of indicator calculated when the energy storage power station is configured at the i-th node using the first type of battery M1 in M;

[0019] Step 1.3: For the m different types of energy storage batteries in M, perform step 1.2 for each type of battery to establish the node configuration scheme index matrix for each type of battery;

[0020] Step 2: For the node configuration scheme index matrices corresponding to the different types of energy storage batteries that have been obtained, perform comprehensive evaluation on each of them to select the case where each type of energy storage battery has the optimal index benefit when configured at different nodes with different capacities in the target power grid, and form an energy storage battery score matrix;

[0021] Step 3: Obtain all battery type score matrices, select the target power grid node with the highest index benefit according to the score matrix, and take the energy storage configuration scheme evaluation index set corresponding to this node as the optimal scheme for this type of energy storage battery to form an optimal scheme index matrix for all battery types; This step is to select the optimal site and capacity scheme for the same type of energy storage battery; ​​​

[0022] Step 3.1, for a total of m different types of energy storage batteries in M, repeat step 2 to obtain a total of m energy storage battery score matrices corresponding to different types of energy storage batteries;

[0023] Step 3.2, for the energy storage battery score matrix corresponding to the first type of energy storage battery, the average value is obtained by averaging the rows, so that the average score vector E of each node can be obtained ave，M1 , the average score vector is calculated as follows:

[0024]

[0025] In the formula, represents the average score vector of each node corresponding to the first type of energy storage battery; represents the average score of each index when the first node is configured with the first type of energy storage battery; represents the node configuration scheme index matrix corresponding to the M1 type of energy storage battery The score obtained by evaluating the data value obtained when the bth node is configured with the energy storage station in the nth index value;

[0026] The maximum value in E ave，M1 is selected as the node score with the optimal benefit, and the maximum value corresponds to the pth node, which is the selected node of this type of energy storage battery in the target power grid. The p1th row vector of the node configuration scheme index matrix D M1 corresponding to this node is taken out as the optimal configuration index set of the first type of energy storage battery, and is used, where p1 represents the optimal node corresponding to the first type of energy storage battery as the p1th node.

[0027] Step 3.3: repeat step 3.2 a total of m times to select the optimal configuration index set for all m types of energy storage batteries, and obtain a total of m optimal configuration index sets, λ p1M1 , λ p2M2 ,..., λ pmMm ;

[0028] Step 4: according to the requirements of the investors, weights are set for the power grid benefit indicators after the configuration of each energy storage station, and the optimal configuration index set of different energy storage batteries is scored and selected according to the planning scheme.

[0029] The present patent integrates the selection of energy storage battery type, site selection and capacity determination in the planning stage of the energy storage station, and tightly couples the three through comprehensive scoring, ensuring the long-term stability and superiority of the planning results. BRIEF DESCRIPTION OF DRAWINGS

[0030] Figure 1is a method flowchart in the embodiments of the present application. DETAILED DESCRIPTION

[0031] Embodiments

[0032] A method for integrated selection, site selection and capacity selection of energy storage power stations, comprising the following processes:

[0033] Step 1: In the process of planning the target power grid energy storage power station, write down the node configuration scheme index matrix of the selectable energy storage battery model, so as to represent the index benefits of different types of batteries when configured at each node in the network; specifically including the following steps:

[0034] Step 1.1: For m types of energy storage batteries as selectable energy storage power station battery types, use M = {M1, M2,..., Mm} to represent the battery type set; m

[0035] Step 1.2: For the first type of battery M1 in M, establish the node configuration scheme index matrix of this type of battery; the matrix establishment steps are as follows:

[0036] Step 1.2.1: Obtain the node information of the total b power grid nodes, the power grid network structure information of the total N power grid lines, obtain the target power grid network structure data, power grid line parameters and target power grid power generation and load power consumption of the total L loads;

[0037] Step 1.2.2: Establish an energy storage power station at the i-th power grid node of the target power grid, input the node information of the total b power grid nodes, the power grid network structure information, the target power grid network structure data, the power grid line parameters and the target power grid power generation and load into the optimal power flow analysis method for calculation, and set the target function as the minimum of the power grid voltage fluctuation, the network loss and the power grid load shedding; the formula is as follows:

[0038]

[0039] In the formula, F min represents the minimized target function composed of the power grid voltage fluctuation, the network loss and the power grid load shedding; k represents a temporary variable in the summation function; b represents the number of target power grid nodes; U k represents the node voltage at the k-th node, U k，N represents the rated node voltage at the k-th node; N represents the total number of target power grid lines; P k represents the line loss of the k-th line; L represents the total number of loads in the target power grid; P l，k represents the actual power consumption of the k-th load; P l，k，N represents the ideal power consumption of the k-th load; ​

[0040] By the optimal power flow analysis method, the capacity of the energy storage power station configured at the i-th node can be obtained to ensure that the power grid operates in the optimal benefit condition;

[0041] Step 1.2.3: After the optimal power flow analysis method is run, n different operating benefit indicators of the power grid can be calculated to form the energy storage configuration scheme evaluation index set λ of the energy storage configured at the i-th node i，M1 1，i，M1 2，i，M1 n，i，M1 , wherein each λ represents a certain indicator, such as a power grid voltage fluctuation indicator, a power grid line loss indicator, a power grid frequency fluctuation indicator, and a power grid load cutting indicator, which together form the energy storage configuration scheme evaluation index set of the energy storage configured at the i-th node;

[0042] Step 1.2.4: For all b power grid nodes in the target power grid, repeat steps 1.2.2 and 1.2.3, so that all b power grid nodes in the power grid respectively generate a corresponding energy storage configuration scheme evaluation index set. These b energy storage configuration scheme evaluation index sets form the node configuration scheme indicator matrix D of the energy storage power station configured using the first type of battery M1 in M M1 ; the node configuration scheme indicator matrix The formula is as follows:

[0043]

[0044] In the formula, represents the node configuration scheme indicator matrix of the energy storage power station configured using the first type of battery M1 in M represents the energy storage configuration scheme evaluation index set of the energy storage power station configured at the i-th node when the first type of battery M1 in M is used to configure the energy storage power station; represents the n-th type of indicator value calculated when the first type of battery M1 in M is used to configure the energy storage power station at the i-th node;

[0045] Step 1.3: For the m different types of energy storage batteries in M, perform step 1.2 respectively to establish the node configuration scheme indicator matrix of each type of battery;

[0046] Step 2: For the node configuration scheme indicator matrix corresponding to the different types of energy storage batteries that have been obtained, perform comprehensive evaluation on it respectively, and select the optimal indicator benefit condition of each type of energy storage battery when configured at different nodes in the target power grid, to form an energy storage battery score matrix; including the following steps:

[0047] ​​​Step 2.1: the jth battery type in M j The corresponding node configuration scheme index matrix D Mj According to the classification of each index as a maximum type index or a minimum type index, the maximum type index means that the larger the index is, the better, and the minimum type index means that the smaller the index is, the better. In order to uniformly calculate different types of indexes, the maximum type index is converted into a minimum type index in this embodiment.

[0048] Step 2.2: different types of indexes in the node configuration scheme index matrix have different dimensions, and these indexes are uniformly standardized. Here, the standardization process of the kth index is taken as an example. At this time, the standardized form of the kth index is converted when the ith node is configured with the energy storage power station corresponding to the M1th energy storage battery type.

[0049] The standardization process is shown in the following formula:

[0050]

[0051] In the formula, λ Nk，i，M1 represents the standardized form of the kth index when the ith node is configured with the energy storage power station corresponding to the M1th energy storage battery type; λ k，i，M1 represents the value calculated by the kth index when the ith node is configured with the energy storage power station corresponding to the M1th energy storage battery type; represents the average value calculated by the kth index when all b nodes are configured with the energy storage power station corresponding to the M1th energy storage battery type, The calculation formula is shown in the following formula:

[0052]

[0053] represents the standard deviation calculated by the kth index when all b nodes are configured with the energy storage power station corresponding to the M1th energy storage battery type, The calculation formula is shown in the following formula:

[0054]

[0055] At this time, the standardized form of the kth index is converted when the ith node is configured with the energy storage power station corresponding to the M1th energy storage battery type.

[0056] Step 2.3: To evaluate the optimal index benefit of the energy storage power station configuration node and capacity configuration scheme in the node configuration scheme index matrix corresponding to the M1th energy storage battery type, the optimal solution and the worst solution of all indexes need to be specified; since all types of indexes have been converted to minimum indexes in step 2.1, the minimum value of each type of index is selected as the optimal solution, and the maximum value of each type of index is selected as the worst solution in the node configuration scheme index matrix, and one optimal solution and one worst solution are selected for each column to form the optimal solution vector Y of the node configuration scheme index matrix corresponding to the M1th energy storage battery type G and the worst solution vector Y W ; the formula is as follows:

[0057]

[0058]

[0059] In the formula, y 1，M1，G represents the optimal solution of the first index corresponding to the M1th energy storage battery type; y 2，M1，G represents the optimal solution of the second index corresponding to the M1th energy storage battery type; y n，M1，G represents the optimal solution of the nth index corresponding to the M1th energy storage battery type; y 1，M1，W represents the optimal solution of the first index corresponding to the M1th energy storage battery type; y 2，M1，W represents the optimal solution of the second index corresponding to the M1th energy storage battery type; y n，M1，W represents the optimal solution of the nth index corresponding to the M1th energy storage battery type;

[0060] Step 2.4: For the node configuration scheme index matrix D Mj of the M1th energy storage battery type, the first column of each element, i.e. the first type of index value when the energy storage power station is configured at each node, is comprehensively scored; the scoring formula is as follows:

[0061]

[0062] In the formula, represents the scoring vector obtained by evaluating the first type of index value of the node configuration scheme index matrix corresponding to the M1th energy storage battery type; represents the scoring obtained by evaluating the data value when the energy storage power station is configured at the first node in the first type of index value of the node configuration scheme index matrix corresponding to the M1th energy storage battery type; represents the scoring obtained by evaluating the data value when the energy storage power station is configured at the first node in the first type of index value of the node configuration scheme index matrix The optimal distance, calculated from the data obtained when configuring an energy storage power station at node 1 in the first category of index values, is shown in the following formula:

[0063]

[0064] In the formula, This represents the index matrix of the node configuration scheme corresponding to the M1th type of energy storage battery. The optimal distance was calculated from the data obtained when configuring the energy storage power station at node 1 in the first category of index values. This represents the value of the first category index calculated when configuring an energy storage power station at the first node using the first category battery M1 in M. This represents the optimal solution for the first metric corresponding to the M1th type of energy storage battery; similarly, we have:

[0065]

[0066] In the formula, This represents the index matrix of the node configuration scheme corresponding to the M1th type of energy storage battery. The worst distance is calculated from the data obtained when the energy storage power station is configured at node 1 in the first category of index values; This represents the value of the first category index calculated when configuring an energy storage power station at the first node using the first category battery M1 in M. This represents the optimal solution for the first index corresponding to the M1th type of energy storage battery;

[0067] Step 2.5: Type j battery M j The corresponding node configuration scheme indicator matrix D Mj This includes a total of n indicator types. For each indicator type, steps 2.1 to 2.4 above are repeated to obtain the scoring vectors corresponding to each of the n indicators. The n scoring vectors form the corresponding score vectors for the j-th type of battery M. j The rating matrix is ​​shown in the following formula:

[0068]

[0069] In the formula, Represents the j-th type of battery M j The corresponding rating matrix, This represents the index matrix of the node configuration scheme corresponding to the M1th type of energy storage battery. The scoring vector obtained after evaluating the values ​​of the first type of indicators; This represents the index matrix of the node configuration scheme corresponding to the M1th type of energy storage battery. a score obtained by evaluating data values obtained when the energy storage power station is configured at the bth node in the nth index value;

[0070] Step 3: Obtain all battery type score matrices, select the target grid node with the highest index benefit according to the score matrix, and take out the energy storage configuration scheme evaluation index set corresponding to the node as the optimal scheme of the type of energy storage battery, to form the optimal scheme index matrix of all battery types. This step is to select the optimal site and capacity scheme for the same type of energy storage battery.

[0071] Step 3.1 For the m different types of energy storage batteries in M, repeat step 2 to obtain m energy storage battery score matrices corresponding to different types of energy storage batteries.

[0072] Step 3.2 For the energy storage battery score matrix of the first type of energy storage battery, take the average value according to the row, so as to obtain the average score vector E ave，M1 of each node. The method for calculating the average score vector E is as follows:

[0073]

[0074] In the formula, E represents the average score vector of each node corresponding to the first type of energy storage battery; represents the average score of each index when the first node configures the first type of energy storage battery; represents the node configuration scheme index matrix corresponding to the M1th type of energy storage battery A score obtained by evaluating data values obtained when the energy storage power station is configured at the bth node in the nth index value;

[0075] Select the maximum value in E ave，M1 as the node score with the optimal benefit. The maximum value corresponds to the pth node, which is the selected node of the type of energy storage battery in the target grid. Take out the p1th row vector of the node configuration scheme index matrix D M1 corresponding to the node as the optimal configuration index set of the first type of energy storage battery, and use to represent, where p1 represents the p1th node corresponding to the optimal node of the first type of energy storage battery.

[0076] Step 3.3: Repeat step 3.2 for a total of m times, and select the optimal configuration index set for each of the m types of energy storage batteries to obtain a total of m optimal configuration index sets λ p1M1， λ p2M2 , …, λ pmMm ;

[0077] Step 4: According to the requirements of the investors, weights of the grid benefits indicators of each energy storage power station after configuration are formulated, and the optimal configuration indicator set of different energy storage batteries is planned according to the scores of the planning schemes; specifically including the following steps:

[0078] Step 4.1: Overall investigation of the requirements of the investors on the configuration of the energy storage power station, comprehensive consideration of the influence and benefits of various indicators on the investors, and formulation of the weight proportion of various indicators in scoring according to the requirements, using W={w1, w2,..., wn} to represent, wherein the sum of the weights of various indicators is 1, i.e. w1+w2+...+wn=1. n n

[0079] Step 4.2: Using the comprehensive evaluation method, the scores of the optimal configuration indicator set of each battery type are evaluated; specifically including the following steps:

[0080] Step 4.2.1: Calculating the mean value of various indicators of the optimal configuration indicator set of all types of energy storage batteries {λ 1，ave , λ 2，ave ,..., λ n，ave};

[0081] The calculation method of the mean value of the first type of indicator is as shown in the following formula:

[0082]

[0083] In the formula, represents the first indicator value in the optimal configuration indicator set of the first type of energy storage battery M1; represents the first indicator value in the optimal configuration indicator set of the second type of energy storage battery M2; λ 1，Mm represents the first indicator value in the optimal configuration indicator set of the mth type of energy storage battery M m ; λ 1，ave represents the mean value of the first type of indicator of the optimal configuration indicator set of all m types of energy storage batteries;

[0084] Step 4.2.2: Repeating step 4.2.1 for all n types of indicators to calculate the mean value of all n types of indicators, i.e. {λ 1，ave , λ 2，ave ,..., λ n，ave};

[0085] Step 4.2.3: Standardizing the n types of indicators to unify the dimensions between different types of indicators; the standardization formula is as shown in the following formula:

[0086]

[0087] In the formula, ​​λ represents the value of the first index in the set of optimal configuration indexes for the first type of energy storage battery M1 after standardization; 1，M1 λ represents the value of the first index in the optimal configuration index set for the first type of energy storage battery M1 before standardization; 1，ave λ represents the mean of the first type of index in the set of optimal configuration indexes for all m types of energy storage batteries; 1，d Let represent the standard deviation of the first type of index in the set of optimal configuration indices for all m types of energy storage batteries. Its calculation is shown in the following formula:

[0088]

[0089] In the formula, λ 1，d λ represents the standard deviation of the first type of index in the set of optimal configuration indicators for all m types of energy storage batteries; 1，M1 λ represents the value of the first index in the optimal configuration index set for the first type of energy storage battery M1 before standardization; 1，ave Let m represent the mean of the first type of index of the optimal configuration index set of all m types of energy storage batteries, where m represents the number of energy storage battery types.

[0090] Step 4.2.4: Repeat step 4.2.3 for all indicators to obtain the standardized optimal configuration indicator set λ for each battery type. p1，NM1 , λ p2，NM2 ,...,λ pm，NMm .in

[0091] Step 4.2.5 For each of the obtained optimal configuration index sets λ p1，NM1 , λ p2，NM2 , …, λ pm，NMm The optimal and worst solutions are selected. Each optimal configuration index set corresponding to different battery types has one first-type index value. The maximum and minimum values ​​among all first-type index values ​​are selected, with the minimum value as the optimal solution and the maximum value as the worst solution. After performing the above operation on all indicators, the optimal solution vector Y can be obtained as follows: G，N With the worst solution vector Y W，N :

[0092] Y G，N ={y 1，G y 2，G , ..., y n，G}

[0093] Y W，N ={y 1，W y 2，W , ..., y n，W},

[0094] In the formula, y 1，GThis represents the optimal solution corresponding to the first type of index, i.e., the minimum value among all first type of indices; y 2，G This represents the optimal solution corresponding to the second type of index, i.e., the minimum value among all second type of indices; y n，G This represents the optimal solution corresponding to the nth type of indicator, which is the minimum value among all nth type indicators; y 1，W This represents the worst-case solution corresponding to the first type of index, i.e., the maximum value among all first-type indices; y 2，W This represents the worst-case solution corresponding to the second type of index, i.e., the maximum value among all second-type indices; y n，W This represents the worst solution corresponding to the nth type of indicator, which is the maximum value among all nth type indicators;

[0095] Step 4.2.6: Based on the calculated optimal solution vector Y G，N With the worst solution vector Y W，N For each optimal configuration index set λ p1，NM1 , λ p2，NM2 ,...,λ pm，NMm Evaluation and scoring will be conducted; the optimal configuration index set for the first type of energy storage battery will be determined. Calculate its score using the following formula.

[0096]

[0097] In the formula, Represents the optimal configuration index set for the first type of energy storage battery. The overall score; This represents the Euclidean distance between the optimal configuration index set and the optimal solution vector of the first type of energy storage battery. This represents the Euclidean distance between the optimal configuration index set and the optimal solution vector of the first type of energy storage battery. and The calculation method is shown in the following formula:

[0098]

[0099]

[0100] In the formula, This represents the Euclidean distance between the optimal configuration index set and the optimal solution vector of the first type of energy storage battery. This represents the Euclidean distance between the optimal configuration index set and the optimal solution vector of the first type of energy storage battery. This represents the value of the first category of indicators in the optimal configuration index set for the first type of energy storage battery; λ represents the value of the first type of index in the optimal configuration index set for the first type of energy storage battery; Nn，M1The values ​​of the first type of index in the optimal configuration index set for the first type of energy storage battery are w1, w2, ..., w n y represents the weights of each of the n categories of indicators. 1，G This represents the optimal solution corresponding to the first type of index, i.e., the minimum value among all first type of indices; y 2，G This represents the optimal solution corresponding to the second type of index, i.e., the minimum value among all second type of indices; y n，G This represents the optimal solution corresponding to the nth type of indicator, which is the minimum value among all nth type indicators; y 1，W This represents the worst-case solution corresponding to the first type of index, i.e., the maximum value among all first-type indices; y 2，W This represents the worst-case solution corresponding to the second type of index, i.e., the maximum value among all second-type indices; y n，W This represents the worst solution corresponding to the nth type of indicator, which is the maximum value among all nth type indicators;

[0101] For a total of m types of energy storage battery optimal configuration index set λ p1，NM1 , λ p2，NM2 ,...,λ pm，NMm After scoring according to the above formula, an m-dimensional column vector S of energy storage battery scores can be obtained. all As shown in the following formula:

[0102]

[0103] In the formula, S all Represents the column vector of energy storage battery ratings; Represents the optimal configuration index set for the first type of energy storage battery. The overall score; Represents the optimal configuration index set for the second type of energy storage battery. The overall score; S Mm Represents the optimal configuration index set for the second type of energy storage battery. The overall score;

[0104] For a total of m types of energy storage battery optimal configuration index set λ p1，NM1 , λ p2，NM2 ,...,λ pm，NMm After scoring according to the above formula, an m-dimensional column vector S of energy storage battery scores can be obtained. all ;

[0105] Step 4.3: Based on the energy storage battery rating column vector S all Based on the scores of each energy storage battery, the optimal configuration index set of the energy storage battery with the highest score can be selected. Here, it is assumed that the optimal configuration index set of the U-th energy storage battery has the highest score S.MU , i.e. the optimal configuration indicator set of the energy storage battery selected by the method is λ pU，NMU , i.e. the Uth type energy storage battery is the energy storage battery type selected by the method, wherein the energy storage power station is configured at the node p U with the most effective benefit, and the energy storage power station capacity result obtained under the most effective operation control when the energy storage power station is configured at the node p U obtained in the optimal power flow analysis in step 1.2.2 is the energy storage power station planning configuration result.

Claims

1. An integrated sizing, siting and sizing method for energy storage power stations, characterized in that, The process comprises the following steps: Step 1: For the target power grid energy storage power station planning process, write down the node configuration scheme index matrix of the selectable energy storage battery models to represent the index benefits of different types of batteries when configured at each node in the network; specifically comprising the following steps: Step 1.1: For m types of energy storage batteries as alternative energy storage plant battery types, use M = {M1, M2, …, Mm} to represent the battery type set. m} represents the battery type set. Step 1.2: For the first type of battery M1 in M, establish the node configuration scheme index matrix of this type of battery; the matrix establishment steps are as follows: Step 1.2.1: Obtain the node information of the target power grid with a total of b power grid nodes, the power grid network structure information of a total of N power grid lines, obtain the target power grid network structure data, power grid line parameters, and target power grid power generation and load power consumption of a total of L loads; Step 1.2.2: Establish an energy storage power station at the i-th power grid node of the target power grid, input the node information of the target power grid with a total of b power grid nodes, the power grid network structure information, the target power grid network structure data, the power grid line parameters, and the target power grid power generation and load into the optimal power flow analysis method for calculation, and set the target function as the minimum power grid voltage fluctuation, the minimum network loss, and the minimum power grid load shedding; the formula is as follows: where F min represents a minimized objective function consisting of grid voltage fluctuation, network loss, and grid load shedding; k represents a temporary variable in the summation function; b represents the number of target grid nodes; U k represents the node voltage at the kth node, U k,N represents the rated node voltage at the kth node; N represents the total number of target grid lines; P k represents the line loss on the kth line; L represents the total number of loads in the target grid; P l,k represents the actual power consumption of the kth load; P l,k,N represents the ideal power consumption of the kth load; Through the optimal power flow analysis method, the capacity of the energy storage power station that needs to be configured at the i-th node to ensure that the power grid operates in the optimal benefit condition can be obtained; Step 1.2.3: After running the optimal power flow analysis method, a total of n different power grid operation efficiency indicators can be calculated, forming the evaluation index set λ of the energy storage configuration scheme when an energy storage power station is configured on the i-th node. i,M1 ={λ 1,i,M1 ,λ 2,i,M1 ,…,λ n,i,M1 }, where each λ represents a certain index, and these indices together form the evaluation index set of the energy storage configuration scheme when configuring an energy storage power station on the i-th node; Step 1.2.4: Repeat Step 1.2.2 and Step 1.2.3 for all b grid nodes in the target grid, so that for each of the b grid nodes, a corresponding set of evaluation indexes of energy storage configuration scheme is generated, and these b sets of evaluation indexes of energy storage configuration scheme form a node configuration scheme index matrix D of the target grid when the energy storage power station is configured using the first type of battery M1 in the Mth type of battery M. M1 ; Node configuration scheme indicator matrix The formula is as follows: In the formula, represents the node configuration scheme index matrix when the energy storage power station is configured using the first type of battery M1 in M represents the energy storage configuration scheme evaluation index set when the energy storage power station is configured at the i th node when the energy storage power station is configured using the first type of battery M1 in M represents the n th type of index value calculated when the energy storage power station is configured at the i th node when the energy storage power station is configured using the first type of battery M1 in M Step 1.3: For the total of m different types of energy storage batteries in M, perform step 1.2 respectively to establish the node configuration scheme index matrix of each type of battery; Step 2: For the node configuration scheme index matrix corresponding to different types of energy storage batteries that have been obtained, respectively, evaluate it comprehensively, select the case with the optimal index benefit when different types of energy storage batteries are configured with different capacities in the target power grid, and form an energy storage battery score matrix; Step 3: Obtain all battery type score matrices, select the target power grid node with the highest index benefit according to the score matrix, and take out the corresponding energy storage configuration scheme evaluation index set as the optimal scheme of this type of energy storage battery to form an optimal scheme index matrix of all battery types; this step is to select the optimal site and capacity scheme for the same type of energy storage battery; Step 3.1: For the total of m different types of energy storage batteries in M, repeat step 2 to obtain a total of m energy storage battery score matrices corresponding to different types of energy storage batteries; Step 3.2 The energy storage battery score matrix corresponding to the energy storage battery of the first type is averaged by row, so that the average score vector E of each node can be obtained ave,M1 The average score vector E is calculated as follows: The method is as follows: wherein, represents the average score vector of each node corresponding to the first type of energy storage battery; represents the average score of each index of the first node when the first type of energy storage battery is configured; represents the node configuration scheme index matrix corresponding to the M1th type of energy storage battery the score obtained by evaluating the data value obtained when the bth node is configured with the n th index value. Select E ave,M1 The maximum value in the maximum value corresponding to the pth node is the node score with the optimal benefit, which is the selected node of the energy storage battery of this type in the target power grid, and the pth row vector of the node configuration scheme index matrix D M1 corresponding to this node is taken out as the optimal configuration index set of the first type of energy storage battery, which is represented by , wherein p1 represents that the optimal node corresponding to the first type of energy storage battery is the p1th node. Step 3.3: Repeat step 3.2 for m times, and select the optimal configuration index set for each of the m energy storage batteries, to obtain m optimal configuration index sets, λ p1M1 , λ p2M2 , …, λ pmMm ; Step 4: According to the requirements of the investors, set the weights of the power grid benefit indexes after the configuration of each energy storage power station, and score and select the optimal configuration index set of different energy storage batteries according to the planning scheme.

2. The method according to claim 1, wherein, Step 2 specifically comprises the following steps: Step 2.1: the jth battery in M j The corresponding node configuration scheme index matrix D Mj According to the classification of each index as a maximum index or a minimum index, the maximum index is converted into a minimum index; Step 2.2: The different types of indexes in the node configuration scheme index matrix have different dimensions, and these indexes are uniformly standardized; When the energy storage power station is configured at the i-th node, the standardized form of the k-th index is converted, The standardization process is shown in the following formula: where λ N k,i,M1 represents the normalized form of the kth index when the energy storage power station is configured at the ith node corresponding to the M1th energy storage battery type. λ k,i,M1 represents the value calculated by the kth index corresponding to the M1st energy storage battery type when the energy storage power station is configured at the ith node; λ k,M1,ave represents the average value calculated by the kth index corresponding to the M1st energy storage battery type when the energy storage power station is configured at all b nodes; λ k,M1,ave The calculation formula is as follows: λ k,M1,d λk represents the standard deviation of the kth index calculated when the energy storage power station is configured at all b nodes corresponding to the M1th energy storage battery type k,M1,d The calculation formula is as follows: At this time, the standardized form of the k-th index when the energy storage power station of the M1th type of energy storage battery is configured at the i-th node is converted; Step 2.3: specify the optimal solution and the worst solution in all indexes; since all indexes have been converted into minimum indexes in step 2.1, the minimum value of each type of index in the node configuration scheme index matrix is selected as the optimal solution, and the maximum value of each type of index is selected as the worst solution. Each column selects an optimal solution and a worst solution to form the optimal solution vector Y of the node configuration scheme index matrix corresponding to the M1th energy storage battery type G and the worst solution vector Y W ; the formula is as follows: where y 1,M1,G represents the optimal solution of the first index corresponding to the M1th energy storage battery type; y 2,M1,G represents the optimal solution of the second index corresponding to the M1th energy storage battery type; y n,M1,G represents the optimal solution of the nth index corresponding to the M1th energy storage battery type; y 1,M1,W represents the optimal solution of the first index corresponding to the M1th energy storage battery type; y 2,M1,W represents the optimal solution of the second index corresponding to the M1th energy storage battery type; y n,M1,W represents the optimal solution of the nth index corresponding to the M1th energy storage battery type; Step 2.4: For the node configuration scheme index matrix D corresponding to the M1th energy storage battery type Mj For each element in the first column, i.e. the first type of index value when configuring the energy storage power station, a comprehensive score is made; the scoring formula is as follows: In the formula, An index matrix corresponding to the M1th energy storage battery type node configuration scheme The score vector obtained after evaluating the first type of index values; An index matrix corresponding to the M1th energy storage battery type node configuration scheme The score obtained by evaluating the data values obtained when the first type of index values are configured at the first node; An index matrix corresponding to the M1th energy storage battery type node configuration scheme The optimal distance calculated from the data values obtained when the first type of index values are configured at the first node, and the calculation formula is shown in the following formula: In the formula, denotes the node configuration scheme index matrix corresponding to the M1th energy storage battery type The optimal distance calculated from the data values obtained when the energy storage power station is configured at the first node in the first type of index value; denotes the first type of index value calculated when the energy storage power station is configured at the first node when the M1th energy storage battery M1 is used to configure the energy storage power station; denotes the optimal solution of the first index corresponding to the M1th energy storage battery type. Similarly, there are: In the formula, denotes the node configuration scheme index matrix corresponding to the M1th energy storage battery type The worst distance calculated from the data value obtained when the energy storage power station is configured at the first node in the first type index value; denotes the first type index value calculated when the energy storage power station is configured at the first node when the first type battery M1 is used to configure the energy storage power station; denotes the optimal solution of the first index corresponding to the M1th energy storage battery type. Step 2.5: the jth battery M j The corresponding node configuration scheme index matrix D Mj Wherein, n kinds of index types are contained, and the above steps 2.1 to 2.4 are repeated for each index type, so that the corresponding score vector of each of the n indexes can be obtained, and the n score vectors constitute a score matrix of the corresponding jth battery M j The score matrix is as follows: In the formula, M represents the jth type of battery j The corresponding score matrix, M represents the M1th type of energy storage battery The score vector obtained after evaluating the first type of index value; M represents the M1th type of energy storage battery The score obtained by evaluating the data value of the nth type of index value when the energy storage power station is configured at the bth node.

3. The method of claim 1, wherein the method is characterized by, Step 4 specifically comprises the following steps: Step 4.1 Overall research on the requirements of the investment party on the configuration of the energy storage power station, comprehensive consideration of the influence and benefit of various indicators on the investment party, and according to this, the weight proportion of various indicators in the scoring is formulated, which is represented as W={w1, w2, …, w n}, wherein the sum of the weights of each indicator is 1, that is, w1+w2+…+w n =1; Step 4.2 adopts the comprehensive evaluation method to evaluate the optimal configuration index set score of each battery type; Step 4.2.1 Calculate the mean value of each index in the optimal configuration index set of all types of energy storage batteries {λ 1,ave ,λ 2,ave ,…,λ n,ave} The calculation method of the first type index average value is as shown in the following formula: In the formula, represents the first index value in the optimal configuration index set of the first type of energy storage battery M1; represents the first index value in the optimal configuration index set of the second type of energy storage battery M2; λ 1,Mm represents the first index value in the optimal configuration index set of the mth type of energy storage battery M m ; λ 1,ave represents the first type index mean value of the optimal configuration index set of all m types of energy storage batteries; Step 4.2.2: Repeat Step 4.2.1 for all n indicators to calculate the indicator mean, i.e., {λ 1,ave , λ 2,ave , …, λ n,ave} for all n indicators. Step 4.2.3 is to unify the dimensions between different types of indexes, and the n indexes are standardized; the standardization formula is as shown in the following formula: In the formula, represents the first index value in the optimal configuration index set of the first type of energy storage battery M1 after standardization; λ 1,M1 represents the first index value in the optimal configuration index set of the first type of energy storage battery M1 before standardization; λ 1,ave represents the first type of index mean value of the optimal configuration index set of all m types of energy storage batteries; λ 1,d represents the first type of index standard deviation of the optimal configuration index set of all m types of energy storage batteries, which is calculated as shown in the following formula: In the formula, λ 1,d denotes the first type index standard deviation of the optimal configuration index set of all m types of energy storage batteries; λ 1,M1 denotes the first index value in the optimal configuration index set of the first type energy storage battery M1 before standardization; λ 1,ave denotes the first type index mean of the optimal configuration index set of all m types of energy storage batteries, and m denotes the number of energy storage battery types. Step 4.2.4: Repeat Step 4.2.3 for all indices to obtain the set of normalized optimal configuration indices λ for each battery type p1,NM1 ,λ p2,NM2 ,…,λ pm,NMm . Where Step 4.2.5 Select the optimal solution and the worst solution from the obtained respective optimal configuration index set λ p1,NM1 , λ p2,NM2 ,..., λ pm,NMm ; each optimal configuration index set corresponding to a different battery type has 1 first type index value, select the maximum value and the minimum value from all the first type index values, the minimum value is the optimal solution, and the maximum value is the worst solution. After performing the above operation on all indexes, the following formula can be obtained: optimal solution vector Y G,N and worst solution vector Y W,N : Y G,N = {y 1,G , y 2,G ,..., y n,G}, Y W,N = {y 1,W ,y 2,W ,...,y n,W}, where y 1,G denotes the optimal solution corresponding to the first type of indicators, i.e. the minimum value among all first type of indicators; y 2,G denotes the optimal solution corresponding to the second type of indicators, i.e. the minimum value among all second type of indicators; y n,G denotes the optimal solution corresponding to the n-th type of indicators, i.e. the minimum value among all n-th type of indicators; y 1,W denotes the worst solution corresponding to the first type of indicators, i.e. the maximum value among all first type of indicators; y 2,W denotes the worst solution corresponding to the second type of indicators, i.e. the maximum value among all second type of indicators; y n,W denotes the worst solution corresponding to the n-th type of indicators, i.e. the maximum value among all n-th type of indicators; Step 4.2.6: According to the calculated optimal solution vector Y G,N and the worst solution vector Y W,N , the optimal configuration indicator set λ p1,NM1 , λ p2,NM2 , …, λ pm,NMm is evaluated and scored; for the optimal configuration indicator set λ of the first type of energy storage battery, its score is calculated using the following formula In the formula, represents the optimal configuration index set of the first type of energy storage battery The comprehensive score of represents the Euclidean distance between the optimal configuration index set of the first type of energy storage battery and the optimal solution vector represents the Euclidean distance between the optimal configuration index set of the first type of energy storage battery and the optimal solution vector and The calculation method is shown in the following formula: In the formula, represents the Euclidean distance between the optimal configuration index set of the first type of energy storage battery and the optimal solution vector; represents the Euclidean distance between the optimal configuration index set of the first type of energy storage battery and the optimal solution vector; represents the first type of index value in the optimal configuration index set of the first type of energy storage battery; represents the first type of index value in the optimal configuration index set of the first type of energy storage battery; λ N n,M1 represents the first type of index value in the optimal configuration index set of the first type of energy storage battery, w1, w2, …, w n represents the respective weights of the n types of indexes, y 1,G represents the optimal solution corresponding to the first type of index, i.e., the minimum value among all the first type of indexes; y 2,G represents the optimal solution corresponding to the second type of index, i.e., the minimum value among all the second type of indexes; y n,G represents the optimal solution corresponding to the nth type of index, i.e., the minimum value among all the nth type of indexes; y 1,W represents the worst solution corresponding to the first type of index, i.e., the maximum value among all the first type of indexes; y 2,W represents the worst solution corresponding to the second type of index, i.e., the maximum value among all the second type of indexes; y n,W represents the worst solution corresponding to the nth type of index, i.e., the maximum value among all the nth type of indexes; An optimal configuration index set λ of m types of energy storage batteries p1,NM1 ,λ p2,NM2 ,…,λ pm,NMm According to the scoring of the above formula, an m-dimensional energy storage battery score column vector S all is obtained, as shown in the following formula: where S all represents the energy storage battery score column vector; represents the optimal configuration indicator set of the first type of energy storage battery comprehensive score of the first type of energy storage battery; represents the optimal configuration indicator set of the second type of energy storage battery comprehensive score of the second type of energy storage battery; S Mm represents the optimal configuration indicator set of the second type of energy storage battery comprehensive score of the second type of energy storage battery; An optimal configuration index set λ of m types of energy storage batteries p1,NM1 ,λ p2,NM2 ,…,λ pm,NMm According to the above formula, an m-dimensional energy storage battery score column vector S all can be obtained Step 4.3: According to the energy storage battery score column vector S all The energy storage battery optimal configuration index set with the highest score S MU , that is, the energy storage battery optimal configuration index set selected by the method is λ pU,NMU , which means that the Uth type of energy storage battery is the energy storage battery type selected by the method, wherein the energy storage power station is configured at the node p U , which has the most effective benefit, and the energy storage power station capacity result obtained in the optimal power flow analysis in step 1.2.2, that is, the energy storage power station capacity result obtained when the energy storage power station is configured at the node p U , has the most effective operation control, which is the energy storage power station planning configuration result.

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